Structured Video-Language Modeling with Temporal Grouping and Spatial Grounding

arXiv:2303.16341v2 Announce Type: replace
Abstract: Existing video-language pre-training methods primarily focus on instance-level alignment between video clips and captions via global contrastive learning but neglect rich fine-grained local information in both videos and text, which is of importance to downstream tasks requiring temporal localization and semantic reasoning. A powerful model is expected to be capable of capturing region-object correspondences and recognizing scene changes in a video clip, reflecting spatial and temporal granularity, respectively. To strengthen model's understanding into such fine-grained …

abstract alignment arxiv captions cs.cv fine-grained focus global importance information instance language localization modeling pre-training reasoning semantic spatial tasks temporal text training type via video videos